{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: Machine Learning Project Part 2\n",
    "\n",
    "在这一系列的笔记本中，我们正在研究一种受监督的回归机器学习问题。 使用真实的纽约市建筑能源数据，我们希望预测建筑物的能源之星得分并确定影响得分的因素。\n",
    "\n",
    "我们使用机器学习管道的大纲来构建我们的项目：\n",
    "\n",
    "1. 数据清理和格式化\n",
    "\n",
    "2. 探索性数据分析\n",
    "\n",
    "3. 特征工程和特征选择\n",
    "\n",
    "4. 在性能指标上比较几种机器学习模型\n",
    "\n",
    "5. 对最佳模型执行超参数调整\n",
    "\n",
    "6. 在测试集合中评估最佳模型\n",
    "\n",
    "7. 解释模型结果\n",
    "\n",
    "8. 得出结论\n",
    "\n",
    "第一个笔记本涵盖了步骤1-3，在这个笔记本中，我们将覆盖4-6。 在本系列中，我更多地关注实现而不是细节，对于那些寻求机器学习方法的更多背景的人， I recommend [Hands-On Machine Learning with Scikit-Learn and Tensorflow](http://shop.oreilly.com/product/0636920052289.do) by Aurelien Geron. This is an excellent resource for the basic theory behind the algorithms and how to use them effectively in Python!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports \n",
    "\n",
    "We will use the standard data science and machine learning libraries in this project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas and numpy for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# No warnings about setting value on copy of slice\n",
    "pd.options.mode.chained_assignment = None\n",
    "pd.set_option('display.max_columns', 60)\n",
    "\n",
    "# Matplotlib for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Set default font size\n",
    "plt.rcParams['font.size'] = 24\n",
    "\n",
    "from IPython.core.pylabtools import figsize\n",
    "\n",
    "# Seaborn for visualization\n",
    "import seaborn as sns\n",
    "sns.set(font_scale = 2)\n",
    "\n",
    "# Imputing missing values and scaling values\n",
    "from sklearn.preprocessing import Imputer, MinMaxScaler\n",
    "\n",
    "# Machine Learning Models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Hyperparameter tuning\n",
    "# 超参数调整\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in Data\n",
    "\n",
    "First let's read in the formatted data from the previous notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Feature Size:  (6622, 64)\n",
      "Testing Feature Size:   (2839, 64)\n",
      "Training Labels Size:   (6622, 1)\n",
      "Testing Labels Size:    (2839, 1)\n"
     ]
    }
   ],
   "source": [
    "# Read in data into dataframes \n",
    "train_features = pd.read_csv('data/training_features.csv')\n",
    "test_features = pd.read_csv('data/testing_features.csv')\n",
    "train_labels = pd.read_csv('data/training_labels.csv')\n",
    "test_labels = pd.read_csv('data/testing_labels.csv')\n",
    "\n",
    "# Display sizes of data\n",
    "print('Training Feature Size: ', train_features.shape)\n",
    "print('Testing Feature Size:  ', test_features.shape)\n",
    "print('Training Labels Size:  ', train_labels.shape)\n",
    "print('Testing Labels Size:   ', test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提醒一下，这是格式化数据的样子。 在第一个笔记本中，我们通过获取变量的自然对数，包括两个分类变量，并通过删除高共线特征来选择特征子集，来设计多个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order</th>\n",
       "      <th>Property Id</th>\n",
       "      <th>DOF Gross Floor Area</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Number of Buildings - Self-reported</th>\n",
       "      <th>Occupancy</th>\n",
       "      <th>Site EUI (kBtu/ft²)</th>\n",
       "      <th>Weather Normalized Site Electricity Intensity (kWh/ft²)</th>\n",
       "      <th>Weather Normalized Site Natural Gas Intensity (therms/ft²)</th>\n",
       "      <th>Water Intensity (All Water Sources) (gal/ft²)</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Community Board</th>\n",
       "      <th>Census Tract</th>\n",
       "      <th>log_Direct GHG Emissions (Metric Tons CO2e)</th>\n",
       "      <th>log_Water Intensity (All Water Sources) (gal/ft²)</th>\n",
       "      <th>Borough_Staten Island</th>\n",
       "      <th>Largest Property Use Type_Adult Education</th>\n",
       "      <th>Largest Property Use Type_Automobile Dealership</th>\n",
       "      <th>Largest Property Use Type_Bank Branch</th>\n",
       "      <th>Largest Property Use Type_College/University</th>\n",
       "      <th>Largest Property Use Type_Convenience Store without Gas Station</th>\n",
       "      <th>Largest Property Use Type_Courthouse</th>\n",
       "      <th>Largest Property Use Type_Distribution Center</th>\n",
       "      <th>Largest Property Use Type_Enclosed Mall</th>\n",
       "      <th>Largest Property Use Type_Financial Office</th>\n",
       "      <th>Largest Property Use Type_Hospital (General Medical &amp; Surgical)</th>\n",
       "      <th>Largest Property Use Type_Hotel</th>\n",
       "      <th>Largest Property Use Type_K-12 School</th>\n",
       "      <th>Largest Property Use Type_Library</th>\n",
       "      <th>...</th>\n",
       "      <th>Largest Property Use Type_Multifamily Housing</th>\n",
       "      <th>Largest Property Use Type_Museum</th>\n",
       "      <th>Largest Property Use Type_Non-Refrigerated Warehouse</th>\n",
       "      <th>Largest Property Use Type_Other</th>\n",
       "      <th>Largest Property Use Type_Other - Education</th>\n",
       "      <th>Largest Property Use Type_Other - Entertainment/Public Assembly</th>\n",
       "      <th>Largest Property Use Type_Other - Lodging/Residential</th>\n",
       "      <th>Largest Property Use Type_Other - Mall</th>\n",
       "      <th>Largest Property Use Type_Other - Public Services</th>\n",
       "      <th>Largest Property Use Type_Other - Recreation</th>\n",
       "      <th>Largest Property Use Type_Other - Services</th>\n",
       "      <th>Largest Property Use Type_Other - Specialty Hospital</th>\n",
       "      <th>Largest Property Use Type_Outpatient Rehabilitation/Physical Therapy</th>\n",
       "      <th>Largest Property Use Type_Parking</th>\n",
       "      <th>Largest Property Use Type_Performing Arts</th>\n",
       "      <th>Largest Property Use Type_Pre-school/Daycare</th>\n",
       "      <th>Largest Property Use Type_Refrigerated Warehouse</th>\n",
       "      <th>Largest Property Use Type_Repair Services (Vehicle, Shoe, Locksmith, etc.)</th>\n",
       "      <th>Largest Property Use Type_Residence Hall/Dormitory</th>\n",
       "      <th>Largest Property Use Type_Residential Care Facility</th>\n",
       "      <th>Largest Property Use Type_Restaurant</th>\n",
       "      <th>Largest Property Use Type_Retail Store</th>\n",
       "      <th>Largest Property Use Type_Self-Storage Facility</th>\n",
       "      <th>Largest Property Use Type_Senior Care Community</th>\n",
       "      <th>Largest Property Use Type_Social/Meeting Hall</th>\n",
       "      <th>Largest Property Use Type_Strip Mall</th>\n",
       "      <th>Largest Property Use Type_Supermarket/Grocery Store</th>\n",
       "      <th>Largest Property Use Type_Urgent Care/Clinic/Other Outpatient</th>\n",
       "      <th>Largest Property Use Type_Wholesale Club/Supercenter</th>\n",
       "      <th>Largest Property Use Type_Worship Facility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13276</td>\n",
       "      <td>5849784</td>\n",
       "      <td>90300.0</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>126.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>99.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.088818</td>\n",
       "      <td>4.599253</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7377</td>\n",
       "      <td>4398442</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>1926</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>95.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.835496</td>\n",
       "      <td>-73.887745</td>\n",
       "      <td>3.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>5.384036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9479</td>\n",
       "      <td>4665374</td>\n",
       "      <td>104700.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>40.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.663206</td>\n",
       "      <td>-73.949469</td>\n",
       "      <td>9.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.017280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14774</td>\n",
       "      <td>3393340</td>\n",
       "      <td>129333.0</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>157.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.622968</td>\n",
       "      <td>-74.078742</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.510853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3286</td>\n",
       "      <td>2704325</td>\n",
       "      <td>109896.0</td>\n",
       "      <td>1927</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>62.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.65</td>\n",
       "      <td>40.782421</td>\n",
       "      <td>-73.972622</td>\n",
       "      <td>7.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>6.123589</td>\n",
       "      <td>3.355153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1060</td>\n",
       "      <td>2430725</td>\n",
       "      <td>182655.0</td>\n",
       "      <td>1929</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>52.9</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.80</td>\n",
       "      <td>40.725136</td>\n",
       "      <td>-74.004438</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.516649</td>\n",
       "      <td>1.568616</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10846</td>\n",
       "      <td>5737475</td>\n",
       "      <td>65400.0</td>\n",
       "      <td>1942</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>66.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.14</td>\n",
       "      <td>40.637833</td>\n",
       "      <td>-73.973045</td>\n",
       "      <td>12.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>5.426271</td>\n",
       "      <td>4.206780</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4280</td>\n",
       "      <td>2670505</td>\n",
       "      <td>113150.0</td>\n",
       "      <td>1938</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>78.4</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.73</td>\n",
       "      <td>40.776035</td>\n",
       "      <td>-73.964418</td>\n",
       "      <td>8.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>6.067036</td>\n",
       "      <td>3.425239</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12974</td>\n",
       "      <td>2964670</td>\n",
       "      <td>137700.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>41.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.170447</td>\n",
       "      <td>3.736717</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13244</td>\n",
       "      <td>4414693</td>\n",
       "      <td>63693.0</td>\n",
       "      <td>1941</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>97.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>86.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.680855</td>\n",
       "      <td>4.464528</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3903</td>\n",
       "      <td>2669664</td>\n",
       "      <td>82644.0</td>\n",
       "      <td>1922</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>55.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762510</td>\n",
       "      <td>-73.970085</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11203.0</td>\n",
       "      <td>1.335001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8336</td>\n",
       "      <td>2809354</td>\n",
       "      <td>51317.0</td>\n",
       "      <td>1925</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>118.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.849372</td>\n",
       "      <td>-73.832075</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26601.0</td>\n",
       "      <td>5.662960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order  Property Id  DOF Gross Floor Area  Year Built  \\\n",
       "0   13276      5849784               90300.0        1950   \n",
       "1    7377      4398442               52000.0        1926   \n",
       "2    9479      4665374              104700.0        1954   \n",
       "3   14774      3393340              129333.0        1992   \n",
       "4    3286      2704325              109896.0        1927   \n",
       "5    1060      2430725              182655.0        1929   \n",
       "6   10846      5737475               65400.0        1942   \n",
       "7    4280      2670505              113150.0        1938   \n",
       "8   12974      2964670              137700.0        1959   \n",
       "9   13244      4414693               63693.0        1941   \n",
       "10   3903      2669664               82644.0        1922   \n",
       "11   8336      2809354               51317.0        1925   \n",
       "\n",
       "    Number of Buildings - Self-reported  Occupancy  Site EUI (kBtu/ft²)  \\\n",
       "0                                     1        100                126.0   \n",
       "1                                     1        100                 95.4   \n",
       "2                                     1        100                 40.4   \n",
       "3                                     1        100                157.1   \n",
       "4                                     1        100                 62.3   \n",
       "5                                     1         90                 52.9   \n",
       "6                                     1        100                 66.8   \n",
       "7                                     1        100                 78.4   \n",
       "8                                     1        100                 63.0   \n",
       "9                                     1        100                 97.8   \n",
       "10                                    1        100                 55.4   \n",
       "11                                    1        100                118.7   \n",
       "\n",
       "    Weather Normalized Site Electricity Intensity (kWh/ft²)  \\\n",
       "0                                                 5.2         \n",
       "1                                                 4.7         \n",
       "2                                                 3.8         \n",
       "3                                                16.9         \n",
       "4                                                 3.5         \n",
       "5                                                 9.7         \n",
       "6                                                 3.0         \n",
       "7                                                 5.7         \n",
       "8                                                 3.4         \n",
       "9                                                 4.3         \n",
       "10                                                4.5         \n",
       "11                                                3.6         \n",
       "\n",
       "    Weather Normalized Site Natural Gas Intensity (therms/ft²)  \\\n",
       "0                                                 1.2            \n",
       "1                                                 0.9            \n",
       "2                                                 0.3            \n",
       "3                                                 1.1            \n",
       "4                                                 0.0            \n",
       "5                                                 0.2            \n",
       "6                                                 0.6            \n",
       "7                                                 NaN            \n",
       "8                                                 0.5            \n",
       "9                                                 0.8            \n",
       "10                                                0.0            \n",
       "11                                                1.1            \n",
       "\n",
       "    Water Intensity (All Water Sources) (gal/ft²)   Latitude  Longitude  \\\n",
       "0                                           99.41        NaN        NaN   \n",
       "1                                             NaN  40.835496 -73.887745   \n",
       "2                                             NaN  40.663206 -73.949469   \n",
       "3                                             NaN  40.622968 -74.078742   \n",
       "4                                           28.65  40.782421 -73.972622   \n",
       "5                                            4.80  40.725136 -74.004438   \n",
       "6                                           67.14  40.637833 -73.973045   \n",
       "7                                           30.73  40.776035 -73.964418   \n",
       "8                                           41.96        NaN        NaN   \n",
       "9                                           86.88        NaN        NaN   \n",
       "10                                            NaN  40.762510 -73.970085   \n",
       "11                                            NaN  40.849372 -73.832075   \n",
       "\n",
       "    Community Board  Census Tract  \\\n",
       "0               NaN           NaN   \n",
       "1               3.0         161.0   \n",
       "2               9.0         329.0   \n",
       "3               1.0          27.0   \n",
       "4               7.0         165.0   \n",
       "5               2.0          37.0   \n",
       "6              12.0         490.0   \n",
       "7               8.0         142.0   \n",
       "8               NaN           NaN   \n",
       "9               NaN           NaN   \n",
       "10              5.0       11203.0   \n",
       "11             10.0       26601.0   \n",
       "\n",
       "    log_Direct GHG Emissions (Metric Tons CO2e)  \\\n",
       "0                                      6.088818   \n",
       "1                                      5.384036   \n",
       "2                                      5.017280   \n",
       "3                                      6.510853   \n",
       "4                                      6.123589   \n",
       "5                                      5.516649   \n",
       "6                                      5.426271   \n",
       "7                                      6.067036   \n",
       "8                                      6.170447   \n",
       "9                                      5.680855   \n",
       "10                                     1.335001   \n",
       "11                                     5.662960   \n",
       "\n",
       "    log_Water Intensity (All Water Sources) (gal/ft²)  Borough_Staten Island  \\\n",
       "0                                            4.599253                      0   \n",
       "1                                                 NaN                      0   \n",
       "2                                                 NaN                      0   \n",
       "3                                                 NaN                      1   \n",
       "4                                            3.355153                      0   \n",
       "5                                            1.568616                      0   \n",
       "6                                            4.206780                      0   \n",
       "7                                            3.425239                      0   \n",
       "8                                            3.736717                      0   \n",
       "9                                            4.464528                      0   \n",
       "10                                                NaN                      0   \n",
       "11                                                NaN                      0   \n",
       "\n",
       "    Largest Property Use Type_Adult Education  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "3                                           0   \n",
       "4                                           0   \n",
       "5                                           0   \n",
       "6                                           0   \n",
       "7                                           0   \n",
       "8                                           0   \n",
       "9                                           0   \n",
       "10                                          0   \n",
       "11                                          0   \n",
       "\n",
       "    Largest Property Use Type_Automobile Dealership  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "7                                                 0   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "10                                                0   \n",
       "11                                                0   \n",
       "\n",
       "    Largest Property Use Type_Bank Branch  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "5                                       0   \n",
       "6                                       0   \n",
       "7                                       0   \n",
       "8                                       0   \n",
       "9                                       0   \n",
       "10                                      0   \n",
       "11                                      0   \n",
       "\n",
       "    Largest Property Use Type_College/University  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "5                                              0   \n",
       "6                                              0   \n",
       "7                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "10                                             0   \n",
       "11                                             0   \n",
       "\n",
       "    Largest Property Use Type_Convenience Store without Gas Station  \\\n",
       "0                                                   0                 \n",
       "1                                                   0                 \n",
       "2                                                   0                 \n",
       "3                                                   0                 \n",
       "4                                                   0                 \n",
       "5                                                   0                 \n",
       "6                                                   0                 \n",
       "7                                                   0                 \n",
       "8                                                   0                 \n",
       "9                                                   0                 \n",
       "10                                                  0                 \n",
       "11                                                  0                 \n",
       "\n",
       "    Largest Property Use Type_Courthouse  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "5                                      0   \n",
       "6                                      0   \n",
       "7                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "10                                     0   \n",
       "11                                     0   \n",
       "\n",
       "    Largest Property Use Type_Distribution Center  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "5                                               0   \n",
       "6                                               0   \n",
       "7                                               0   \n",
       "8                                               0   \n",
       "9                                               0   \n",
       "10                                              0   \n",
       "11                                              0   \n",
       "\n",
       "    Largest Property Use Type_Enclosed Mall  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "5                                         0   \n",
       "6                                         0   \n",
       "7                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "10                                        0   \n",
       "11                                        0   \n",
       "\n",
       "    Largest Property Use Type_Financial Office  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "5                                            0   \n",
       "6                                            0   \n",
       "7                                            0   \n",
       "8                                            0   \n",
       "9                                            0   \n",
       "10                                           0   \n",
       "11                                           0   \n",
       "\n",
       "    Largest Property Use Type_Hospital (General Medical & Surgical)  \\\n",
       "0                                                   0                 \n",
       "1                                                   0                 \n",
       "2                                                   0                 \n",
       "3                                                   0                 \n",
       "4                                                   0                 \n",
       "5                                                   0                 \n",
       "6                                                   0                 \n",
       "7                                                   0                 \n",
       "8                                                   0                 \n",
       "9                                                   0                 \n",
       "10                                                  0                 \n",
       "11                                                  0                 \n",
       "\n",
       "    Largest Property Use Type_Hotel  Largest Property Use Type_K-12 School  \\\n",
       "0                                 0                                      0   \n",
       "1                                 0                                      0   \n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "4                                 0                                      0   \n",
       "5                                 0                                      0   \n",
       "6                                 0                                      0   \n",
       "7                                 0                                      0   \n",
       "8                                 0                                      0   \n",
       "9                                 0                                      0   \n",
       "10                                0                                      0   \n",
       "11                                0                                      0   \n",
       "\n",
       "    Largest Property Use Type_Library  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "5                                   0   \n",
       "6                                   0   \n",
       "7                                   0   \n",
       "8                                   0   \n",
       "9                                   0   \n",
       "10                                  0   \n",
       "11                                  0   \n",
       "\n",
       "                       ...                      \\\n",
       "0                      ...                       \n",
       "1                      ...                       \n",
       "2                      ...                       \n",
       "3                      ...                       \n",
       "4                      ...                       \n",
       "5                      ...                       \n",
       "6                      ...                       \n",
       "7                      ...                       \n",
       "8                      ...                       \n",
       "9                      ...                       \n",
       "10                     ...                       \n",
       "11                     ...                       \n",
       "\n",
       "    Largest Property Use Type_Multifamily Housing  \\\n",
       "0                                               1   \n",
       "1                                               1   \n",
       "2                                               1   \n",
       "3                                               0   \n",
       "4                                               1   \n",
       "5                                               0   \n",
       "6                                               1   \n",
       "7                                               1   \n",
       "8                                               1   \n",
       "9                                               1   \n",
       "10                                              1   \n",
       "11                                              1   \n",
       "\n",
       "    Largest Property Use Type_Museum  \\\n",
       "0                                  0   \n",
       "1                                  0   \n",
       "2                                  0   \n",
       "3                                  0   \n",
       "4                                  0   \n",
       "5                                  0   \n",
       "6                                  0   \n",
       "7                                  0   \n",
       "8                                  0   \n",
       "9                                  0   \n",
       "10                                 0   \n",
       "11                                 0   \n",
       "\n",
       "    Largest Property Use Type_Non-Refrigerated Warehouse  \\\n",
       "0                                                   0      \n",
       "1                                                   0      \n",
       "2                                                   0      \n",
       "3                                                   0      \n",
       "4                                                   0      \n",
       "5                                                   0      \n",
       "6                                                   0      \n",
       "7                                                   0      \n",
       "8                                                   0      \n",
       "9                                                   0      \n",
       "10                                                  0      \n",
       "11                                                  0      \n",
       "\n",
       "    Largest Property Use Type_Other  \\\n",
       "0                                 0   \n",
       "1                                 0   \n",
       "2                                 0   \n",
       "3                                 0   \n",
       "4                                 0   \n",
       "5                                 0   \n",
       "6                                 0   \n",
       "7                                 0   \n",
       "8                                 0   \n",
       "9                                 0   \n",
       "10                                0   \n",
       "11                                0   \n",
       "\n",
       "    Largest Property Use Type_Other - Education  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "5                                             0   \n",
       "6                                             0   \n",
       "7                                             0   \n",
       "8                                             0   \n",
       "9                                             0   \n",
       "10                                            0   \n",
       "11                                            0   \n",
       "\n",
       "    Largest Property Use Type_Other - Entertainment/Public Assembly  \\\n",
       "0                                                   0                 \n",
       "1                                                   0                 \n",
       "2                                                   0                 \n",
       "3                                                   0                 \n",
       "4                                                   0                 \n",
       "5                                                   0                 \n",
       "6                                                   0                 \n",
       "7                                                   0                 \n",
       "8                                                   0                 \n",
       "9                                                   0                 \n",
       "10                                                  0                 \n",
       "11                                                  0                 \n",
       "\n",
       "    Largest Property Use Type_Other - Lodging/Residential  \\\n",
       "0                                                   0       \n",
       "1                                                   0       \n",
       "2                                                   0       \n",
       "3                                                   0       \n",
       "4                                                   0       \n",
       "5                                                   0       \n",
       "6                                                   0       \n",
       "7                                                   0       \n",
       "8                                                   0       \n",
       "9                                                   0       \n",
       "10                                                  0       \n",
       "11                                                  0       \n",
       "\n",
       "    Largest Property Use Type_Other - Mall  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "7                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "10                                       0   \n",
       "11                                       0   \n",
       "\n",
       "    Largest Property Use Type_Other - Public Services  \\\n",
       "0                                                   0   \n",
       "1                                                   0   \n",
       "2                                                   0   \n",
       "3                                                   0   \n",
       "4                                                   0   \n",
       "5                                                   0   \n",
       "6                                                   0   \n",
       "7                                                   0   \n",
       "8                                                   0   \n",
       "9                                                   0   \n",
       "10                                                  0   \n",
       "11                                                  0   \n",
       "\n",
       "    Largest Property Use Type_Other - Recreation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "5                                              0   \n",
       "6                                              0   \n",
       "7                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "10                                             0   \n",
       "11                                             0   \n",
       "\n",
       "    Largest Property Use Type_Other - Services  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "5                                            0   \n",
       "6                                            0   \n",
       "7                                            0   \n",
       "8                                            0   \n",
       "9                                            0   \n",
       "10                                           0   \n",
       "11                                           0   \n",
       "\n",
       "    Largest Property Use Type_Other - Specialty Hospital  \\\n",
       "0                                                   0      \n",
       "1                                                   0      \n",
       "2                                                   0      \n",
       "3                                                   0      \n",
       "4                                                   0      \n",
       "5                                                   0      \n",
       "6                                                   0      \n",
       "7                                                   0      \n",
       "8                                                   0      \n",
       "9                                                   0      \n",
       "10                                                  0      \n",
       "11                                                  0      \n",
       "\n",
       "    Largest Property Use Type_Outpatient Rehabilitation/Physical Therapy  \\\n",
       "0                                                   0                      \n",
       "1                                                   0                      \n",
       "2                                                   0                      \n",
       "3                                                   0                      \n",
       "4                                                   0                      \n",
       "5                                                   0                      \n",
       "6                                                   0                      \n",
       "7                                                   0                      \n",
       "8                                                   0                      \n",
       "9                                                   0                      \n",
       "10                                                  0                      \n",
       "11                                                  0                      \n",
       "\n",
       "    Largest Property Use Type_Parking  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "5                                   0   \n",
       "6                                   0   \n",
       "7                                   0   \n",
       "8                                   0   \n",
       "9                                   0   \n",
       "10                                  0   \n",
       "11                                  0   \n",
       "\n",
       "    Largest Property Use Type_Performing Arts  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "3                                           0   \n",
       "4                                           0   \n",
       "5                                           0   \n",
       "6                                           0   \n",
       "7                                           0   \n",
       "8                                           0   \n",
       "9                                           0   \n",
       "10                                          0   \n",
       "11                                          0   \n",
       "\n",
       "    Largest Property Use Type_Pre-school/Daycare  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "5                                              0   \n",
       "6                                              0   \n",
       "7                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "10                                             0   \n",
       "11                                             0   \n",
       "\n",
       "    Largest Property Use Type_Refrigerated Warehouse  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "5                                                  0   \n",
       "6                                                  0   \n",
       "7                                                  0   \n",
       "8                                                  0   \n",
       "9                                                  0   \n",
       "10                                                 0   \n",
       "11                                                 0   \n",
       "\n",
       "    Largest Property Use Type_Repair Services (Vehicle, Shoe, Locksmith, etc.)  \\\n",
       "0                                                   0                            \n",
       "1                                                   0                            \n",
       "2                                                   0                            \n",
       "3                                                   0                            \n",
       "4                                                   0                            \n",
       "5                                                   0                            \n",
       "6                                                   0                            \n",
       "7                                                   0                            \n",
       "8                                                   0                            \n",
       "9                                                   0                            \n",
       "10                                                  0                            \n",
       "11                                                  0                            \n",
       "\n",
       "    Largest Property Use Type_Residence Hall/Dormitory  \\\n",
       "0                                                   0    \n",
       "1                                                   0    \n",
       "2                                                   0    \n",
       "3                                                   0    \n",
       "4                                                   0    \n",
       "5                                                   0    \n",
       "6                                                   0    \n",
       "7                                                   0    \n",
       "8                                                   0    \n",
       "9                                                   0    \n",
       "10                                                  0    \n",
       "11                                                  0    \n",
       "\n",
       "    Largest Property Use Type_Residential Care Facility  \\\n",
       "0                                                   0     \n",
       "1                                                   0     \n",
       "2                                                   0     \n",
       "3                                                   0     \n",
       "4                                                   0     \n",
       "5                                                   0     \n",
       "6                                                   0     \n",
       "7                                                   0     \n",
       "8                                                   0     \n",
       "9                                                   0     \n",
       "10                                                  0     \n",
       "11                                                  0     \n",
       "\n",
       "    Largest Property Use Type_Restaurant  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "5                                      0   \n",
       "6                                      0   \n",
       "7                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "10                                     0   \n",
       "11                                     0   \n",
       "\n",
       "    Largest Property Use Type_Retail Store  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "7                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "10                                       0   \n",
       "11                                       0   \n",
       "\n",
       "    Largest Property Use Type_Self-Storage Facility  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "7                                                 0   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "10                                                0   \n",
       "11                                                0   \n",
       "\n",
       "    Largest Property Use Type_Senior Care Community  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 1   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "7                                                 0   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "10                                                0   \n",
       "11                                                0   \n",
       "\n",
       "    Largest Property Use Type_Social/Meeting Hall  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "5                                               0   \n",
       "6                                               0   \n",
       "7                                               0   \n",
       "8                                               0   \n",
       "9                                               0   \n",
       "10                                              0   \n",
       "11                                              0   \n",
       "\n",
       "    Largest Property Use Type_Strip Mall  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "5                                      0   \n",
       "6                                      0   \n",
       "7                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "10                                     0   \n",
       "11                                     0   \n",
       "\n",
       "    Largest Property Use Type_Supermarket/Grocery Store  \\\n",
       "0                                                   0     \n",
       "1                                                   0     \n",
       "2                                                   0     \n",
       "3                                                   0     \n",
       "4                                                   0     \n",
       "5                                                   0     \n",
       "6                                                   0     \n",
       "7                                                   0     \n",
       "8                                                   0     \n",
       "9                                                   0     \n",
       "10                                                  0     \n",
       "11                                                  0     \n",
       "\n",
       "    Largest Property Use Type_Urgent Care/Clinic/Other Outpatient  \\\n",
       "0                                                   0               \n",
       "1                                                   0               \n",
       "2                                                   0               \n",
       "3                                                   0               \n",
       "4                                                   0               \n",
       "5                                                   0               \n",
       "6                                                   0               \n",
       "7                                                   0               \n",
       "8                                                   0               \n",
       "9                                                   0               \n",
       "10                                                  0               \n",
       "11                                                  0               \n",
       "\n",
       "    Largest Property Use Type_Wholesale Club/Supercenter  \\\n",
       "0                                                   0      \n",
       "1                                                   0      \n",
       "2                                                   0      \n",
       "3                                                   0      \n",
       "4                                                   0      \n",
       "5                                                   0      \n",
       "6                                                   0      \n",
       "7                                                   0      \n",
       "8                                                   0      \n",
       "9                                                   0      \n",
       "10                                                  0      \n",
       "11                                                  0      \n",
       "\n",
       "    Largest Property Use Type_Worship Facility  \n",
       "0                                            0  \n",
       "1                                            0  \n",
       "2                                            0  \n",
       "3                                            0  \n",
       "4                                            0  \n",
       "5                                            0  \n",
       "6                                            0  \n",
       "7                                            0  \n",
       "8                                            0  \n",
       "9                                            0  \n",
       "10                                           0  \n",
       "11                                           0  \n",
       "\n",
       "[12 rows x 64 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `score` 列包含能源之星分数，这是我们机器学习问题的目标。 能源之星评分应该是对建筑物能效的一种比较测量，尽管我们看到第一部分的计算方法可能存在问题！\n",
    "\n",
    "这是能源之星得分的分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b347308ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(8, 8)\n",
    "\n",
    "# Histogram of the Energy Star Score\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.hist(train_labels['score'].dropna(), bins = 100);\n",
    "plt.xlabel('Score'); plt.ylabel('Number of Buildings'); \n",
    "plt.title('ENERGY Star Score Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Evaluating and Comparing Machine Learning Models\n",
    "We are comparing models using the __mean absolute error__. A baseline model that guessed the median value of the score was off by an __average of 25 points__. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本节中，我们将为我们的监督回归任务构建，训练和评估几种机器学习方法。 目标是确定哪个模型最有希望进一步开发（例如超参数调整）。\n",
    "我们使用平均绝对误差比较模型。 猜测得分中值的基线模型平均偏离25分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Imputing Missing Values-输入缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "标准机器学习模型无法处理缺失值，这意味着我们必须找到一种方法来填充这些缺失值或丢弃任何具有缺失值的特征。 由于我们已经删除了第一部分中缺失值超过50％的要素，因此我们将重点关注这些缺失值，即称为插补 ([imputation](https://en.wikipedia.org/wiki/Imputation_(statistics)))的过程。 有许多插补方法，但在这里我们将使用相对简单的方法用列的**中位数**替换缺失值。([Here is a more thorough discussion on imputing missing values](http://www.stat.columbia.edu/~gelman/arm/missing.pdf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在下面的代码中，我们创建一个Scikit-learn Imputer对象，用列的中位数填充缺失值。 请注意，我们在**训练数据**上训练了imputer（使用Imputer.fit方法），但没有训练测试数据。 然后我们转换（使用Imputer.transform）训练数据和测试数据。 这意味着**测试集**中的缺失值用训练集中相应列的中值填充。 [我们必须这样做](https://stackoverflow.com/a/46692001)而不是在部署时，是因为我们必须根据以前的训练数据将缺失值归入新观察中。 这是避免称为[数据泄漏](https://www.kaggle.com/dansbecker/data-leakage)的问题的一种方法，其中来自测试集的信息“泄漏”到训练过程中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an imputer object with a median filling strategy\n",
    "imputer = Imputer(strategy='median')\n",
    "\n",
    "# Train on the training features\n",
    "imputer.fit(train_features)\n",
    "\n",
    "# Transform both training data and testing data\n",
    "X = imputer.transform(train_features)\n",
    "X_test = imputer.transform(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in training features:  0\n",
      "Missing values in testing features:   0\n"
     ]
    }
   ],
   "source": [
    "print('Missing values in training features: ', np.sum(np.isnan(X)))\n",
    "print('Missing values in testing features:  ', np.sum(np.isnan(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([], dtype=int64), array([], dtype=int64))\n",
      "(array([], dtype=int64), array([], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "# Make sure all values are finite\n",
    "print(np.where(~np.isfinite(X)))\n",
    "print(np.where(~np.isfinite(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在插补后，所有特征都是实值的。 对于更复杂的插补方法（尽管中值通常很有效），请查看 [this article](https://www.tandfonline.com/doi/full/10.1080/1743727X.2014.979146)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Scaling Features- 特征缩放"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在我们构建模型之前要采取的最后一步是[特征缩放](https://en.wikipedia.org/wiki/Feature_scaling)。这是必要的，因为要素具有不同的单位，我们希望对要素进行标准化，以使单位不影响算法。[线性回归和随机森林不需要特征缩放](https://stats.stackexchange.com/questions/121886/when-should-i-apply-feature-scaling-for-my-data)但其他方法（例如支持向量机和k-最近邻）确实需要它，因为它们考虑了观测之间的欧氏距离。因此，在比较多个算法时，最佳做法是特征缩放。\n",
    "\n",
    "有两种[特征缩放](http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html)的方法：\n",
    "\n",
    "* 对于每个值，减去特征的平均值并除以特征的标准偏差。这称为标准化，并且导致每个特征具有0的均值和1的标准偏差。\n",
    "* 对于每个值，减去要素的最小值并除以最大值减去要素的最小值（范围）。这可以确保要素的所有值都在0到1之间，并称为缩放到范围或标准化。\n",
    "这是一篇关于[normalization and standardization](https://machinelearningmastery.com/normalize-standardize-machine-learning-data-weka/).的好文章\n",
    "\n",
    "与插补一样，当我们训练缩放对象时，我们只想使用训练集。当我们转换特征时，我们将转换训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the scaler object with a range of 0-1\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "# Fit on the training data\n",
    "scaler.fit(X)\n",
    "\n",
    "# Transform both the training and testing data\n",
    "X = scaler.transform(X)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert y to one-dimensional array (vector)\n",
    "y = np.array(train_labels).reshape((-1, ))\n",
    "y_test = np.array(test_labels).reshape((-1, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Models to Evaluate-要评估的模型\n",
    "\n",
    "我们将使用Scikit-Learn库比较五种不同的机器学习模型 [Scikit-Learn library](http://scikit-learn.org/stable/):：\n",
    "\n",
    "* 线性回归\n",
    "* 支持向量机回归\n",
    "* 随机森林回归\n",
    "* Gradient Boosting 回归\n",
    "* K-Nearest Neighbors回归\n",
    "\n",
    "同样，在这里，我专注于实现，而不是解释这些是如何工作的。 除了Hands-On Machine Learning之外，另一个用于阅读机器学习模型的优秀资源 [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/).\n",
    "\n",
    "为了比较模型，我们将主要使用Scikit-Learn默认的模型超参数值。 通常这些将表现得很好，但应该在实际使用模型之前进行优化。 首先，我们只想确定每个模型的baseline性能，然后我们可以选择性能最佳的模型，以便使用超参数调整进行进一步优化。 请记住，默认的超参数将启动并运行模型，但几乎总是应该使用某种搜索来调整以找到问题的最佳设置！ \n",
    "\n",
    "Here is what the Scikit-learn documentation [says about the defaults](https://arxiv.org/abs/1309.0238):\n",
    "\n",
    "    __Sensible defaults__: Whenever an operation requires a user-defined parameter,\n",
    "    an appropriate default value is defined by the library. The default value\n",
    "    should cause the operation to be performed in a sensible way (giving a baseline\n",
    "    solution for the task at hand.)\n",
    "\n",
    "关于scikit-learn的最好的一点是**所有模型都以相同的方式实现**：一旦你知道如何构建一个模型，你就可以实现**一系列**极其多样化的模型。 在这里，我们将在几行代码中实现许多模型的整个训练和测试程序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate mean absolute error\n",
    "def mae(y_true, y_pred):\n",
    "    return np.mean(abs(y_true - y_pred))\n",
    "\n",
    "# Takes in a model, trains the model, and evaluates the model on the test set\n",
    "def fit_and_evaluate(model):\n",
    "    \n",
    "    # Train the model\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    # Make predictions and evalute\n",
    "    model_pred = model.predict(X_test)\n",
    "    model_mae = mae(y_test, model_pred)\n",
    "    \n",
    "    # Return the performance metric\n",
    "    return model_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Performance on the test set: MAE = 13.4651\n"
     ]
    }
   ],
   "source": [
    "# 线性回归\n",
    "lr = LinearRegression()\n",
    "lr_mae = fit_and_evaluate(lr)\n",
    "\n",
    "print('Linear Regression Performance on the test set: MAE = %0.4f' % lr_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Regression Performance on the test set: MAE = 10.9337\n"
     ]
    }
   ],
   "source": [
    "# 支持向量机\n",
    "svm = SVR(C = 1000, gamma = 0.1)\n",
    "svm_mae = fit_and_evaluate(svm)\n",
    "\n",
    "print('Support Vector Machine Regression Performance on the test set: MAE = %0.4f' % svm_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regression Performance on the test set: MAE = 10.0147\n"
     ]
    }
   ],
   "source": [
    "#随机森林\n",
    "random_forest = RandomForestRegressor(random_state=60)\n",
    "random_forest_mae = fit_and_evaluate(random_forest)\n",
    "\n",
    "print('Random Forest Regression Performance on the test set: MAE = %0.4f' % random_forest_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosted Regression Performance on the test set: MAE = 10.0132\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting Regression\n",
    "gradient_boosted = GradientBoostingRegressor(random_state=60)\n",
    "gradient_boosted_mae = fit_and_evaluate(gradient_boosted)\n",
    "\n",
    "print('Gradient Boosted Regression Performance on the test set: MAE = %0.4f' % gradient_boosted_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors Regression Performance on the test set: MAE = 13.0131\n"
     ]
    }
   ],
   "source": [
    "# K-Nearest Neighbors Regression\n",
    "knn = KNeighborsRegressor(n_neighbors=10)\n",
    "knn_mae = fit_and_evaluate(knn)\n",
    "\n",
    "print('K-Nearest Neighbors Regression Performance on the test set: MAE = %0.4f' % knn_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b34855b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "figsize(6, 4)\n",
    "\n",
    "# Dataframe to hold the results\n",
    "model_comparison = pd.DataFrame({'model': ['Linear Regression', 'Support Vector Machine',\n",
    "                                           'Random Forest', 'Gradient Boosted',\n",
    "                                            'K-Nearest Neighbors'],\n",
    "                                 'mae': [lr_mae, svm_mae, random_forest_mae, \n",
    "                                         gradient_boosted_mae, knn_mae]})\n",
    "\n",
    "# Horizontal bar chart of test mae\n",
    "model_comparison.sort_values('mae', ascending = False).plot(x = 'model', y = 'mae', kind = 'barh',\n",
    "                                                           color = 'red', edgecolor = 'black')\n",
    "\n",
    "# Plot formatting\n",
    "plt.ylabel(''); plt.yticks(size = 14); plt.xlabel('Mean Absolute Error'); plt.xticks(size = 14)\n",
    "plt.title('Model Comparison on Test MAE', size = 20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据运行情况（每次精确结果略有变化），梯度增强回归量表现最佳，其次是随机森林。 我不得不承认这不是最公平的比较，因为我们主要使用默认的超参数。 特别是对于支持向量回归器，超参数对性能有重要影响。 （随机森林和梯度增强方法非常适合开始，因为性能较少依赖于模型设置）。 尽管如此，从这些结果中，我们可以得出结论:**机器学习是适用的**，因为所有模型都明显**优于基线**！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从这里开始，我将专注于使用超参数调优来优化最佳模型。 鉴于此处的结果，我将专注于使用**GradientBoostingRegressor**。 这是Gradient Boosted Trees的Scikit-Learn实现[Gradient Boosted Trees](http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf)，在过去的几年中赢得了许多Kaggle比赛[Kaggle competitions](http://matthewemery.ca/Why-Kagglers-Love-XGBoost/) 。 Scikit-Learn版本通常比XGBoost版本慢，但在这里我们将坚持使用Scikit-Learn，因为语法更为熟悉。 [这是](https://www.kaggle.com/dansbecker/learning-to-use-xgboost/code) 在XGBoost包中使用实现的指南。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.  Model Optimization -对最佳模型执行超参数调整，即优化模型\n",
    "\n",
    "In machine learning, optimizing a model means finding the best set of hyperparameters for a particular problem. \n",
    "\n",
    "## 5.1 Hyperparameters-超参数\n",
    "\n",
    "模型超参数定义[model hyperparameters are in contrast to model parameters](https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/) :\n",
    "\n",
    "**模型超参数**被认为最好通过机器学习算法来进行设置，在训练之前由数据科学家调整。 例如，随机森林中的树木数量，或者K-Nearest Neighbors Regression中使用的邻居数量。\n",
    "**模型参数**是模型在训练期间学习的内容，例如线性回归中的权重。\n",
    "\n",
    "我们作为数据科学家通过**选择超参数**来控制模型，这些选择会对模型的最终性能产生显着影响（尽管通常不会像获取更多数据或工程特征那样有效）。\n",
    "\n",
    " \n",
    "调整模型超参数 [Tuning the model hyperparameters](http://scikit-learn.org/stable/modules/grid_search.html) 可以控制模型中欠拟合与过拟合的平衡。 \n",
    "* 我们可以尝试通过制作**更复杂的模型**来校正欠拟合，例如在随机森林中使用更多树或在深度神经网络中使用更多层。 当我们的模型没有足够的容量（自由度）来学习特征和目标之间的关系时，模型会发生**欠拟合并且具有高偏差**。 \n",
    "* 我们可以通过**限制模型的复杂性和应用正则化**来尝试纠正过度拟合。 这可能意味着降低多项式回归的程度，或将衰退层添加到深度神经网络。 **过拟合的模型具有高方差**并且实际上记住了训练集。 \n",
    "\n",
    "**欠拟合和过拟合导致模型在测试集上的泛化性能变差**。\n",
    "\n",
    "选择超参数的问题在于，没有放之四海而皆准的超参数。 因此，对于每个新数据集，我们必须找到最佳设置。 这可能是一个耗时的过程，但幸运的是，在Scikit-Learn中执行此过程有多种选择。更好的是，新的libraries，如epistasis实验室的[TPOT](https://epistasislab.github.io/tpot/) 旨在为您自动完成此过程！ 目前，我们将坚持在Scikit-Learn中手动（有点）这样做，但请继续关注自动模型选择的文章！"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Hyperparameter Tuning with Random Search and Cross Validation- \n",
    "\n",
    "## 随机搜索和交叉验证的超参数调整\n",
    "\n",
    "我们可以通过**随机搜索**和**交叉验证**为模型选择最佳超参数： \n",
    "\n",
    "* **随机搜索**是指我们选择超参数来评估的方法：我们定义一系列选项，然后随机选择要尝试的组合。这与网格搜索形成对比，在网格搜索评估，我们指定每个组合。通常，当我们对最佳模型超参数的知识有限时，随机搜索会更好，我们可以使用随机搜索缩小选项范围，然后使用更有限的选项范围进行网格搜索。 \n",
    "\n",
    "* **交叉验证**是用于评估超参数性能的方法。我们使用K-Fold交叉验证，而不是将训练设置拆分为单独的训练和验证集，以减少我们可以使用的训练数据量。这意味着将训练数据划分为K个折叠，然后进行迭代过程，我们首先在折叠的K-1上进行训练，然后评估第K个折叠的性能。我们重复这个过程K次，所以最终我们将测试训练数据中的每个例子，关键是每次迭代我们都在测试我们之前没有训练过的数据。在K-Fold交叉验证结束时，我们将每个K次迭代的平均误差作为最终性能度量，然后立即在所有训练数据上训练模型。然后，我们记录的性能用于比较超参数的不同组合。 \n",
    "\n",
    "A picture of k-fold cross validation using k = 5 is shown below:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Of course we don't actually do this iteration ourselves, we let Scikit-Learn and `RandomizedSearchCV` do the process for us! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里，我们将使用**交叉验证实现随机搜索**，以选择gradient boosting regressor的最佳超参数：\n",
    "* 我们首先定义一个网格然后执行迭代过程：从网格中随机抽样一组超参数，使用4倍交叉验证评估超参数\n",
    "* 然后选择具有最佳性能的超参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss function to be optimized\n",
    "# 需要被优化的损失函数\n",
    "loss = ['ls', 'lad', 'huber']\n",
    "\n",
    "# Number of trees used in the boosting process\n",
    "# 梯度增强 过程中使用的树的数量\n",
    "n_estimators = [100, 500, 900, 1100, 1500]\n",
    "\n",
    "# Maximum depth of each tree\n",
    "# 树的最大深度\n",
    "max_depth = [2, 3, 5, 10, 15]\n",
    "\n",
    "# Minimum number of samples per leaf\n",
    "# 每片叶子的最小样本数\n",
    "min_samples_leaf = [1, 2, 4, 6, 8]\n",
    "\n",
    "# Minimum number of samples to split a node\n",
    "# 拆分节点的最小样本数\n",
    "min_samples_split = [2, 4, 6, 10]\n",
    "\n",
    "# Maximum number of features to consider for making splits\n",
    "# 进行拆分时要考虑的最大特征数\n",
    "max_features = ['auto', 'sqrt', 'log2', None]\n",
    "\n",
    "# Define the grid of hyperparameters to search\n",
    "hyperparameter_grid = {'loss': loss,\n",
    "                       'n_estimators': n_estimators,\n",
    "                       'max_depth': max_depth,\n",
    "                       'min_samples_leaf': min_samples_leaf,\n",
    "                       'min_samples_split': min_samples_split,\n",
    "                       'max_features': max_features}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们选择了6个不同的超参数来调整梯度增强回归量。 这些都将以不同的方式影响模型，这些方法很难提前确定，找到特定问题的最佳组合的唯一方法是测试它们！ 要了解超参数，我建议您查看Scikit-Learn(http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor)文档。 现在，只要知道我们正在努力寻找超参数的最佳组合，并且因为没有理论告诉我们哪种方法效果最好，我们只需要评估它们，就像运行实验一样！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在下面的代码中，我们创建了随机化搜索对象，传递以下参数：\n",
    "\n",
    "* `estimator`: the model-模型\n",
    "* `param_distributions`: the distribution of parameters we defined-我们定义的参数的分布\n",
    "* `cv` ：the number of folds to use for k-fold cross validation-用于k-fold交叉验证的folds 数量\n",
    "* `n_iter`: the number of different combinations to try-不同的参数组合的数量\n",
    "* `scoring`: which metric to use when evaluating candidates-评估候选参数时使用的指标\n",
    "* `n_jobs`: number of cores to run in parallel (-1 will use all available)-核的数量（-1 时全部使用）\n",
    "* `verbose`: how much information to display (1 displays a limited amount) -显示信息的数量\n",
    "* `return_train_score`: return the training score for each cross-validation fold- 每一个cross-validation fold 返回的分数\n",
    "* `random_state`: fixes the random number generator used so we get the same results every run-修复使用的随机数生成器，因此每次运行都会得到相同的结果\n",
    "\n",
    "随机搜索目标的训练方式与任何其他scikit-learn模型相同。训练之后，我们可以比较所有不同的超参数组合，找到效果最好的组合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model to use for hyperparameter tuning\n",
    "model = GradientBoostingRegressor(random_state = 42)\n",
    "\n",
    "# Set up the random search with 4-fold cross validation\n",
    "random_cv = RandomizedSearchCV(estimator=model,\n",
    "                               param_distributions=hyperparameter_grid,\n",
    "                               cv=4, n_iter=25, \n",
    "                               scoring = 'neg_mean_absolute_error',\n",
    "                               n_jobs = -1, verbose = 1, \n",
    "                               return_train_score = True,\n",
    "                               random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 25 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  5.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=4, error_score='raise',\n",
       "          estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=25, n_jobs=-1,\n",
       "          param_distributions={'min_samples_split': [2, 4, 6, 10], 'loss': ['ls', 'lad', 'huber'], 'n_estimators': [100, 500, 900, 1100, 1500], 'max_depth': [2, 3, 5, 10, 15], 'max_features': ['auto', 'sqrt', 'log2', None], 'min_samples_leaf': [1, 2, 4, 6, 8]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring='neg_mean_absolute_error',\n",
       "          verbose=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit on the training data\n",
    "random_cv.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn使用负平均绝对误差进行评估，因为它希望度量最大化。 因此，更好的分数将接近0.我们可以将随机搜索的结果导入数据帧，并按性能对值进行排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_loss</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.258196</td>\n",
       "      <td>0.023431</td>\n",
       "      <td>-8.998761</td>\n",
       "      <td>-6.893790</td>\n",
       "      <td>lad</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>{'min_samples_split': 6, 'loss': 'lad', 'n_est...</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.859381</td>\n",
       "      <td>-7.027368</td>\n",
       "      <td>-8.773966</td>\n",
       "      <td>-6.837958</td>\n",
       "      <td>-9.325139</td>\n",
       "      <td>-6.869084</td>\n",
       "      <td>-9.036779</td>\n",
       "      <td>-6.840749</td>\n",
       "      <td>0.228464</td>\n",
       "      <td>7.811129e-03</td>\n",
       "      <td>0.210901</td>\n",
       "      <td>0.078077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.943364</td>\n",
       "      <td>0.029172</td>\n",
       "      <td>-9.041004</td>\n",
       "      <td>-4.465253</td>\n",
       "      <td>huber</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>{'min_samples_split': 6, 'loss': 'huber', 'n_e...</td>\n",
       "      <td>2</td>\n",
       "      <td>-8.868788</td>\n",
       "      <td>-4.482073</td>\n",
       "      <td>-8.904791</td>\n",
       "      <td>-4.326121</td>\n",
       "      <td>-9.315304</td>\n",
       "      <td>-4.823009</td>\n",
       "      <td>-9.075321</td>\n",
       "      <td>-4.229809</td>\n",
       "      <td>0.343895</td>\n",
       "      <td>4.425041e-03</td>\n",
       "      <td>0.176509</td>\n",
       "      <td>0.225313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.642810</td>\n",
       "      <td>0.016033</td>\n",
       "      <td>-9.192518</td>\n",
       "      <td>-6.987506</td>\n",
       "      <td>huber</td>\n",
       "      <td>3</td>\n",
       "      <td>auto</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>{'min_samples_split': 4, 'loss': 'huber', 'n_e...</td>\n",
       "      <td>3</td>\n",
       "      <td>-9.134318</td>\n",
       "      <td>-7.007604</td>\n",
       "      <td>-9.042461</td>\n",
       "      <td>-7.088986</td>\n",
       "      <td>-9.441179</td>\n",
       "      <td>-6.913108</td>\n",
       "      <td>-9.152240</td>\n",
       "      <td>-6.940327</td>\n",
       "      <td>0.187064</td>\n",
       "      <td>5.466960e-04</td>\n",
       "      <td>0.149456</td>\n",
       "      <td>0.067939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.849271</td>\n",
       "      <td>0.009556</td>\n",
       "      <td>-9.196413</td>\n",
       "      <td>-7.293554</td>\n",
       "      <td>ls</td>\n",
       "      <td>5</td>\n",
       "      <td>auto</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'min_samples_split': 2, 'loss': 'ls', 'n_esti...</td>\n",
       "      <td>4</td>\n",
       "      <td>-9.101610</td>\n",
       "      <td>-7.302003</td>\n",
       "      <td>-9.026912</td>\n",
       "      <td>-7.312645</td>\n",
       "      <td>-9.456259</td>\n",
       "      <td>-7.173397</td>\n",
       "      <td>-9.201033</td>\n",
       "      <td>-7.386171</td>\n",
       "      <td>0.119780</td>\n",
       "      <td>6.548299e-03</td>\n",
       "      <td>0.162211</td>\n",
       "      <td>0.076569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.494540</td>\n",
       "      <td>0.015621</td>\n",
       "      <td>-9.350555</td>\n",
       "      <td>-7.073741</td>\n",
       "      <td>ls</td>\n",
       "      <td>3</td>\n",
       "      <td>auto</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>{'min_samples_split': 4, 'loss': 'ls', 'n_esti...</td>\n",
       "      <td>5</td>\n",
       "      <td>-9.146159</td>\n",
       "      <td>-7.089709</td>\n",
       "      <td>-9.199358</td>\n",
       "      <td>-7.103749</td>\n",
       "      <td>-9.699696</td>\n",
       "      <td>-7.021418</td>\n",
       "      <td>-9.357220</td>\n",
       "      <td>-7.080088</td>\n",
       "      <td>0.071737</td>\n",
       "      <td>6.165552e-07</td>\n",
       "      <td>0.215966</td>\n",
       "      <td>0.031358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>77.458288</td>\n",
       "      <td>0.140599</td>\n",
       "      <td>-9.376652</td>\n",
       "      <td>-0.400401</td>\n",
       "      <td>huber</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1100</td>\n",
       "      <td>{'min_samples_split': 10, 'loss': 'huber', 'n_...</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.261256</td>\n",
       "      <td>-0.378166</td>\n",
       "      <td>-9.243867</td>\n",
       "      <td>-0.459465</td>\n",
       "      <td>-9.528538</td>\n",
       "      <td>-0.393491</td>\n",
       "      <td>-9.473096</td>\n",
       "      <td>-0.370483</td>\n",
       "      <td>2.343129</td>\n",
       "      <td>1.104526e-02</td>\n",
       "      <td>0.125816</td>\n",
       "      <td>0.035092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.682175</td>\n",
       "      <td>0.023432</td>\n",
       "      <td>-9.381693</td>\n",
       "      <td>-8.202307</td>\n",
       "      <td>lad</td>\n",
       "      <td>3</td>\n",
       "      <td>auto</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1100</td>\n",
       "      <td>{'min_samples_split': 6, 'loss': 'lad', 'n_est...</td>\n",
       "      <td>7</td>\n",
       "      <td>-9.379335</td>\n",
       "      <td>-8.311796</td>\n",
       "      <td>-9.382576</td>\n",
       "      <td>-8.658903</td>\n",
       "      <td>-9.473980</td>\n",
       "      <td>-7.820534</td>\n",
       "      <td>-9.290880</td>\n",
       "      <td>-8.017995</td>\n",
       "      <td>0.310738</td>\n",
       "      <td>7.810712e-03</td>\n",
       "      <td>0.064740</td>\n",
       "      <td>0.316303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.892420</td>\n",
       "      <td>0.060910</td>\n",
       "      <td>-9.419115</td>\n",
       "      <td>-0.165987</td>\n",
       "      <td>huber</td>\n",
       "      <td>10</td>\n",
       "      <td>auto</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>{'min_samples_split': 4, 'loss': 'huber', 'n_e...</td>\n",
       "      <td>8</td>\n",
       "      <td>-9.385744</td>\n",
       "      <td>-0.164859</td>\n",
       "      <td>-9.288317</td>\n",
       "      <td>-0.193613</td>\n",
       "      <td>-9.572674</td>\n",
       "      <td>-0.164415</td>\n",
       "      <td>-9.429825</td>\n",
       "      <td>-0.141062</td>\n",
       "      <td>1.229820</td>\n",
       "      <td>8.957649e-03</td>\n",
       "      <td>0.102366</td>\n",
       "      <td>0.018629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>26.022289</td>\n",
       "      <td>0.042959</td>\n",
       "      <td>-9.446787</td>\n",
       "      <td>-4.801009</td>\n",
       "      <td>ls</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1500</td>\n",
       "      <td>{'min_samples_split': 4, 'loss': 'ls', 'n_esti...</td>\n",
       "      <td>9</td>\n",
       "      <td>-9.246573</td>\n",
       "      <td>-4.821677</td>\n",
       "      <td>-9.317156</td>\n",
       "      <td>-4.876202</td>\n",
       "      <td>-9.798950</td>\n",
       "      <td>-4.776650</td>\n",
       "      <td>-9.424668</td>\n",
       "      <td>-4.729506</td>\n",
       "      <td>0.515063</td>\n",
       "      <td>6.764172e-03</td>\n",
       "      <td>0.212942</td>\n",
       "      <td>0.054284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.132085</td>\n",
       "      <td>0.029833</td>\n",
       "      <td>-9.460295</td>\n",
       "      <td>-5.666205</td>\n",
       "      <td>huber</td>\n",
       "      <td>5</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>{'min_samples_split': 6, 'loss': 'huber', 'n_e...</td>\n",
       "      <td>10</td>\n",
       "      <td>-9.393840</td>\n",
       "      <td>-5.684121</td>\n",
       "      <td>-9.289175</td>\n",
       "      <td>-5.742441</td>\n",
       "      <td>-9.750806</td>\n",
       "      <td>-5.571781</td>\n",
       "      <td>-9.407503</td>\n",
       "      <td>-5.666476</td>\n",
       "      <td>0.078161</td>\n",
       "      <td>1.453753e-03</td>\n",
       "      <td>0.173829</td>\n",
       "      <td>0.061337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "12      15.258196         0.023431        -8.998761         -6.893790   \n",
       "3       14.943364         0.029172        -9.041004         -4.465253   \n",
       "9        8.642810         0.016033        -9.192518         -6.987506   \n",
       "0        1.849271         0.009556        -9.196413         -7.293554   \n",
       "7        7.494540         0.015621        -9.350555         -7.073741   \n",
       "10      77.458288         0.140599        -9.376652         -0.400401   \n",
       "19      17.682175         0.023432        -9.381693         -8.202307   \n",
       "2       46.892420         0.060910        -9.419115         -0.165987   \n",
       "16      26.022289         0.042959        -9.446787         -4.801009   \n",
       "21       5.132085         0.029833        -9.460295         -5.666205   \n",
       "\n",
       "   param_loss param_max_depth param_max_features param_min_samples_leaf  \\\n",
       "12        lad               5               None                      6   \n",
       "3       huber               5               None                      8   \n",
       "9       huber               3               auto                      2   \n",
       "0          ls               5               auto                      6   \n",
       "7          ls               3               auto                      6   \n",
       "10      huber              10               None                      6   \n",
       "19        lad               3               auto                      2   \n",
       "2       huber              10               auto                      2   \n",
       "16         ls               3               None                      6   \n",
       "21      huber               5               log2                      4   \n",
       "\n",
       "   param_min_samples_split param_n_estimators  \\\n",
       "12                       6                500   \n",
       "3                        6                500   \n",
       "9                        4                500   \n",
       "0                        2                100   \n",
       "7                        4                500   \n",
       "10                      10               1100   \n",
       "19                       6               1100   \n",
       "2                        4                500   \n",
       "16                       4               1500   \n",
       "21                       6                500   \n",
       "\n",
       "                                               params  rank_test_score  \\\n",
       "12  {'min_samples_split': 6, 'loss': 'lad', 'n_est...                1   \n",
       "3   {'min_samples_split': 6, 'loss': 'huber', 'n_e...                2   \n",
       "9   {'min_samples_split': 4, 'loss': 'huber', 'n_e...                3   \n",
       "0   {'min_samples_split': 2, 'loss': 'ls', 'n_esti...                4   \n",
       "7   {'min_samples_split': 4, 'loss': 'ls', 'n_esti...                5   \n",
       "10  {'min_samples_split': 10, 'loss': 'huber', 'n_...                6   \n",
       "19  {'min_samples_split': 6, 'loss': 'lad', 'n_est...                7   \n",
       "2   {'min_samples_split': 4, 'loss': 'huber', 'n_e...                8   \n",
       "16  {'min_samples_split': 4, 'loss': 'ls', 'n_esti...                9   \n",
       "21  {'min_samples_split': 6, 'loss': 'huber', 'n_e...               10   \n",
       "\n",
       "    split0_test_score  split0_train_score  split1_test_score  \\\n",
       "12          -8.859381           -7.027368          -8.773966   \n",
       "3           -8.868788           -4.482073          -8.904791   \n",
       "9           -9.134318           -7.007604          -9.042461   \n",
       "0           -9.101610           -7.302003          -9.026912   \n",
       "7           -9.146159           -7.089709          -9.199358   \n",
       "10          -9.261256           -0.378166          -9.243867   \n",
       "19          -9.379335           -8.311796          -9.382576   \n",
       "2           -9.385744           -0.164859          -9.288317   \n",
       "16          -9.246573           -4.821677          -9.317156   \n",
       "21          -9.393840           -5.684121          -9.289175   \n",
       "\n",
       "    split1_train_score  split2_test_score  split2_train_score  \\\n",
       "12           -6.837958          -9.325139           -6.869084   \n",
       "3            -4.326121          -9.315304           -4.823009   \n",
       "9            -7.088986          -9.441179           -6.913108   \n",
       "0            -7.312645          -9.456259           -7.173397   \n",
       "7            -7.103749          -9.699696           -7.021418   \n",
       "10           -0.459465          -9.528538           -0.393491   \n",
       "19           -8.658903          -9.473980           -7.820534   \n",
       "2            -0.193613          -9.572674           -0.164415   \n",
       "16           -4.876202          -9.798950           -4.776650   \n",
       "21           -5.742441          -9.750806           -5.571781   \n",
       "\n",
       "    split3_test_score  split3_train_score  std_fit_time  std_score_time  \\\n",
       "12          -9.036779           -6.840749      0.228464    7.811129e-03   \n",
       "3           -9.075321           -4.229809      0.343895    4.425041e-03   \n",
       "9           -9.152240           -6.940327      0.187064    5.466960e-04   \n",
       "0           -9.201033           -7.386171      0.119780    6.548299e-03   \n",
       "7           -9.357220           -7.080088      0.071737    6.165552e-07   \n",
       "10          -9.473096           -0.370483      2.343129    1.104526e-02   \n",
       "19          -9.290880           -8.017995      0.310738    7.810712e-03   \n",
       "2           -9.429825           -0.141062      1.229820    8.957649e-03   \n",
       "16          -9.424668           -4.729506      0.515063    6.764172e-03   \n",
       "21          -9.407503           -5.666476      0.078161    1.453753e-03   \n",
       "\n",
       "    std_test_score  std_train_score  \n",
       "12        0.210901         0.078077  \n",
       "3         0.176509         0.225313  \n",
       "9         0.149456         0.067939  \n",
       "0         0.162211         0.076569  \n",
       "7         0.215966         0.031358  \n",
       "10        0.125816         0.035092  \n",
       "19        0.064740         0.316303  \n",
       "2         0.102366         0.018629  \n",
       "16        0.212942         0.054284  \n",
       "21        0.173829         0.061337  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all of the cv results and sort by the test performance\n",
    "random_results = pd.DataFrame(random_cv.cv_results_).sort_values('mean_test_score', ascending = False)\n",
    "\n",
    "random_results.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='lad', max_depth=5, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=6,\n",
       "             min_samples_split=6, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=500, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best gradient boosted model has the following hyperparameters:\n",
    "\n",
    "* `loss = lad`\n",
    "* `n_estimators = 500`\n",
    "* `max_depth = 5`\n",
    "* `min_samples_leaf = 6`\n",
    "* `min_samples_split = 6`\n",
    "* `max_features = None` (This means that `max_features = n_features` according to the docs)\n",
    "\n",
    "Here we will use grid search with a grid that only has the `n_estimators` hyperparameter. We will evaluate a range of trees then plot the training and testing performance to get an idea of what increasing the number of trees does for our model. We will fix the other hyperparameters at the best values returned from random search to isolate the number of trees effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用随机搜索是缩小可能的超参数以尝试的好方法。最初，我们不知道哪种组合效果最好，但这至少缩小了选项的范围。\n",
    "\n",
    "我们可以使用随机搜索结果来通过创建具有超参数的网格来通知网格搜索，这些参数接近于在随机搜索期间最佳的参数。但是，我不会再次评估所有这些设置，而是将**重点放在**单个树林中的树木数量（n_estimators）上。通过仅改变一个超参数，我们可以直接观察它如何影响性能。在树木数量的情况下，我们预计会看到对欠拟合和过拟合的影响。\n",
    "\n",
    "在这里，我们将使用**仅具有n_estimators超参数的网格进行网格搜索**。我们将评估一系列树木，然后绘制训练和测试性能，以了解增加树木数量对模型的影响。我们将其他超参数修复为随机搜索返回的最佳值，以隔离树的效果数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a range of trees to evaluate\n",
    "trees_grid = {'n_estimators': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800]}\n",
    "\n",
    "model = GradientBoostingRegressor(loss = 'lad', max_depth = 5,\n",
    "                                  min_samples_leaf = 6,\n",
    "                                  min_samples_split = 6,\n",
    "                                  max_features = None,\n",
    "                                  random_state = 42)\n",
    "\n",
    "# Grid Search Object using the trees range and the random forest model\n",
    "grid_search = GridSearchCV(estimator = model, param_grid=trees_grid, cv = 4, \n",
    "                           scoring = 'neg_mean_absolute_error', verbose = 1,\n",
    "                           n_jobs = -1, return_train_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 15 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   49.4s\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, error_score='raise',\n",
       "       estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='lad', max_depth=5, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=6,\n",
       "             min_samples_split=6, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_absolute_error', verbose=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b34852a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the results into a dataframe\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "# Plot the training and testing error vs number of trees\n",
    "figsize(8, 8)\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.plot(results['param_n_estimators'], -1 * results['mean_test_score'], label = 'Testing Error')\n",
    "plt.plot(results['param_n_estimators'], -1 * results['mean_train_score'], label = 'Training Error')\n",
    "plt.xlabel('Number of Trees'); plt.ylabel('Mean Abosolute Error'); plt.legend();\n",
    "plt.title('Performance vs Number of Trees');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19.183177</td>\n",
       "      <td>0.025855</td>\n",
       "      <td>-8.981504</td>\n",
       "      <td>-6.601028</td>\n",
       "      <td>800</td>\n",
       "      <td>{'n_estimators': 800}</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.825018</td>\n",
       "      <td>-6.739448</td>\n",
       "      <td>-8.757257</td>\n",
       "      <td>-6.648109</td>\n",
       "      <td>-9.297520</td>\n",
       "      <td>-6.486269</td>\n",
       "      <td>-9.046451</td>\n",
       "      <td>-6.530287</td>\n",
       "      <td>0.388542</td>\n",
       "      <td>0.005918</td>\n",
       "      <td>0.211453</td>\n",
       "      <td>0.099437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.416527</td>\n",
       "      <td>0.031242</td>\n",
       "      <td>-8.982193</td>\n",
       "      <td>-6.633542</td>\n",
       "      <td>750</td>\n",
       "      <td>{'n_estimators': 750}</td>\n",
       "      <td>2</td>\n",
       "      <td>-8.830294</td>\n",
       "      <td>-6.772908</td>\n",
       "      <td>-8.750657</td>\n",
       "      <td>-6.660453</td>\n",
       "      <td>-9.299357</td>\n",
       "      <td>-6.520818</td>\n",
       "      <td>-9.048696</td>\n",
       "      <td>-6.579989</td>\n",
       "      <td>0.461739</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.213125</td>\n",
       "      <td>0.094501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22.140391</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>-8.989100</td>\n",
       "      <td>-6.686214</td>\n",
       "      <td>700</td>\n",
       "      <td>{'n_estimators': 700}</td>\n",
       "      <td>3</td>\n",
       "      <td>-8.835777</td>\n",
       "      <td>-6.812928</td>\n",
       "      <td>-8.754819</td>\n",
       "      <td>-6.695770</td>\n",
       "      <td>-9.313318</td>\n",
       "      <td>-6.611240</td>\n",
       "      <td>-9.052721</td>\n",
       "      <td>-6.624919</td>\n",
       "      <td>0.371103</td>\n",
       "      <td>0.002580</td>\n",
       "      <td>0.216536</td>\n",
       "      <td>0.079884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.467533</td>\n",
       "      <td>0.026009</td>\n",
       "      <td>-8.994138</td>\n",
       "      <td>-6.835031</td>\n",
       "      <td>550</td>\n",
       "      <td>{'n_estimators': 550}</td>\n",
       "      <td>4</td>\n",
       "      <td>-8.853451</td>\n",
       "      <td>-7.003413</td>\n",
       "      <td>-8.755897</td>\n",
       "      <td>-6.781878</td>\n",
       "      <td>-9.325442</td>\n",
       "      <td>-6.809121</td>\n",
       "      <td>-9.041991</td>\n",
       "      <td>-6.745712</td>\n",
       "      <td>0.127403</td>\n",
       "      <td>0.006377</td>\n",
       "      <td>0.217136</td>\n",
       "      <td>0.099783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20.311160</td>\n",
       "      <td>0.039053</td>\n",
       "      <td>-8.995319</td>\n",
       "      <td>-6.740194</td>\n",
       "      <td>650</td>\n",
       "      <td>{'n_estimators': 650}</td>\n",
       "      <td>5</td>\n",
       "      <td>-8.850857</td>\n",
       "      <td>-6.864375</td>\n",
       "      <td>-8.754361</td>\n",
       "      <td>-6.721452</td>\n",
       "      <td>-9.325111</td>\n",
       "      <td>-6.708191</td>\n",
       "      <td>-9.051181</td>\n",
       "      <td>-6.666758</td>\n",
       "      <td>0.232313</td>\n",
       "      <td>0.013528</td>\n",
       "      <td>0.218405</td>\n",
       "      <td>0.074480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "14      19.183177         0.025855        -8.981504         -6.601028   \n",
       "13      23.416527         0.031242        -8.982193         -6.633542   \n",
       "12      22.140391         0.035156        -8.989100         -6.686214   \n",
       "9       17.467533         0.026009        -8.994138         -6.835031   \n",
       "11      20.311160         0.039053        -8.995319         -6.740194   \n",
       "\n",
       "   param_n_estimators                 params  rank_test_score  \\\n",
       "14                800  {'n_estimators': 800}                1   \n",
       "13                750  {'n_estimators': 750}                2   \n",
       "12                700  {'n_estimators': 700}                3   \n",
       "9                 550  {'n_estimators': 550}                4   \n",
       "11                650  {'n_estimators': 650}                5   \n",
       "\n",
       "    split0_test_score  split0_train_score  split1_test_score  \\\n",
       "14          -8.825018           -6.739448          -8.757257   \n",
       "13          -8.830294           -6.772908          -8.750657   \n",
       "12          -8.835777           -6.812928          -8.754819   \n",
       "9           -8.853451           -7.003413          -8.755897   \n",
       "11          -8.850857           -6.864375          -8.754361   \n",
       "\n",
       "    split1_train_score  split2_test_score  split2_train_score  \\\n",
       "14           -6.648109          -9.297520           -6.486269   \n",
       "13           -6.660453          -9.299357           -6.520818   \n",
       "12           -6.695770          -9.313318           -6.611240   \n",
       "9            -6.781878          -9.325442           -6.809121   \n",
       "11           -6.721452          -9.325111           -6.708191   \n",
       "\n",
       "    split3_test_score  split3_train_score  std_fit_time  std_score_time  \\\n",
       "14          -9.046451           -6.530287      0.388542        0.005918   \n",
       "13          -9.048696           -6.579989      0.461739        0.000001   \n",
       "12          -9.052721           -6.624919      0.371103        0.002580   \n",
       "9           -9.041991           -6.745712      0.127403        0.006377   \n",
       "11          -9.051181           -6.666758      0.232313        0.013528   \n",
       "\n",
       "    std_test_score  std_train_score  \n",
       "14        0.211453         0.099437  \n",
       "13        0.213125         0.094501  \n",
       "12        0.216536         0.079884  \n",
       "9         0.217136         0.099783  \n",
       "11        0.218405         0.074480  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values('mean_test_score', ascending = False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从这个情节来看，很明显我们的模型**过拟合**了！训练误差明显低于测试误差，这表明模型正在很好地学习训练数据，但是也无法推广到测试数据。随着树木数量的增加，过拟合的数量会增加。随着树木数量的增加，测试和训练误差都会减少，但训练误差会更快地减少。\n",
    "\n",
    "训练误差和测试误差之间始终存在差异（训练误差始终较低）但如果存在显着差异，我们希望通过**获取更多训练数据或降低模型的复杂性**来尝试减少过拟合通过超参数调整或正则化。对于gradient boosting regressor，一些选项包括:\n",
    "* 减少树的数量\n",
    "* 减少每棵树的最大深度\n",
    "* 以及增加叶节点中的最小样本数。\n",
    "\n",
    "对于任何想要进一步深入渐变增强回归量的人来说，**[这是一篇很棒的文章]**(http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/)。目前，我们将使用具有最佳性能的模型，并接受它可能过拟合到训练集。\n",
    "\n",
    "根据交叉验证结果，使用800树的最佳模型，并在9以下达到交叉验证误差。这表明能源之星得分的平均交叉验证估计在真实答案的9分之内！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Evaluate Final Model on the Test Set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将使用超参数调整中的最佳模型来对测试集进行预测。 请记住，我们的模型之前从未见过测试集，所以这个性能应该是模型在现实世界中部署时的表现的一个很好的指标。\n",
    "\n",
    "为了比较，我们还可以查看默认模型的性能。 下面的代码创建最终模型，训练它（带时间），并评估测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='lad', max_depth=5, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=6,\n",
       "             min_samples_split=6, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=800, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Default model\n",
    "default_model = GradientBoostingRegressor(random_state = 42)\n",
    "\n",
    "# Select the best model\n",
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "final_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "727 ms ± 29 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 1 -r 5\n",
    "default_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.6 s ± 231 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 1 -r 5\n",
    "final_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default model performance on the test set: MAE = 10.0118.\n",
      "Final model performance on the test set:   MAE = 9.0446.\n"
     ]
    }
   ],
   "source": [
    "default_pred = default_model.predict(X_test)\n",
    "final_pred = final_model.predict(X_test)\n",
    "\n",
    "print('Default model performance on the test set: MAE = %0.4f.' % mae(y_test, default_pred))\n",
    "print('Final model performance on the test set:   MAE = %0.4f.' % mae(y_test, final_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最终的模型比基线模型的性能提高了大约10％，但代价是显着增加了运行时间（在我的机器上慢了大约12倍）。 机器学习通常是一个权衡领域：\n",
    "* **偏差与方差**\n",
    "* **准确性与可解释性**\n",
    "* **准确性与运行时间**\n",
    "* **以及使用哪种模型** \n",
    "\n",
    "的最终决定取决于具体情况。 这里，运行时间的增加不是障碍，因为虽然相对差异很大，但训练时间的绝对量值并不显着。 在不同的情况下，平衡可能不一样，因此我们需要考虑我们正在优化的内容以及我们必须使用的限制。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了了解预测，我们可以绘制测试集上的真值分布和测试集上的预测值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b347601470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(8, 8)\n",
    "\n",
    "# Density plot of the final predictions and the test values\n",
    "sns.kdeplot(final_pred, label = 'Predictions')\n",
    "sns.kdeplot(y_test, label = 'Values')\n",
    "\n",
    "# Label the plot\n",
    "plt.xlabel('Energy Star Score'); plt.ylabel('Density');\n",
    "plt.title('Test Values and Predictions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然预测值的密度更接近测试值的中值而不是100的实际峰值，但分布看起来几乎相同。看起来模型在预测极值时可能不太准确，而是预测值 更接近中位数。\n",
    "\n",
    "另一个诊断图是残差的直方图。 理想情况下，我们希望残差是正态分布的，这意味着模型在两个方向（高和低）上都是错误的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b347b46c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize = (6, 6)\n",
    "\n",
    "# Calculate the residuals \n",
    "# 计算残差\n",
    "residuals = final_pred - y_test\n",
    "\n",
    "# Plot the residuals in a histogram\n",
    "# 绘制残差分布直方图\n",
    "plt.hist(residuals, color = 'red', bins = 20,\n",
    "         edgecolor = 'black')\n",
    "plt.xlabel('Error'); plt.ylabel('Count')\n",
    "plt.title('Distribution of Residuals');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "残差接近正态分布，低端有一些明显的异常值。 这些表示模型估计远低于真实值的误差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "\n",
    "在个笔记中，我们介绍了机器学习流程中的关键概念：\n",
    "\n",
    "* 输入缺失值\n",
    "* 评估和比较几种机器学习方法\n",
    "* 超参数使用随机搜索和交叉验证来调整机器学习模型\n",
    "* 评估测试集上的最佳模型\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果表明:\n",
    "* 机器学习适用于我们的问题，最终模型能够将建筑物的能源之星得分预测到9.1分以内。\n",
    "* 我们还看到，超级参数调整能够改善模型的性能，尽管在投入的时间方面成本相当高。这是一个很好的提醒，正确的特征工程和收集更多数据（如果可能！）比微调模型有更大的回报。\n",
    "* 我们还观察了运行时与精度之间的权衡，这是我们在设计机器学习模型时必须考虑的众多因素之一。\n",
    "\n",
    "我们知道我们的模型是准确的，但我们知道为什么它能做出预测？机器学习过程的下一步至关重要：尝试理解模型如何进行预测。实现高精度是很好的，但如果我们能够找出模型能够准确预测的原因，那么我们也可以使用这些信息来更好地理解问题。例如，模型依靠哪些功能来推断能源之星得分？可以使用此模型进行特征选择，并实现更易于解释的更简单模型吗？\n",
    "\n",
    "在最后的笔记本中，我们将尝试回答这些问题并从项目中得出最终结论！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
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 "nbformat": 4,
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